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Random CapsNet forest model for imbalanced malware type classification task
(Elsevier, 2021)
Behavior of malware varies depending the malware types, which affects the strategies of the system protection software. Many malware classification models, empowered by machine and/or deep learning, achieve superior ...
A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data
(Mdpi, 2020)
Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease ...
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy
(Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020)
Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data ...
Air quality prediction using CNN plus LSTM-based hybrid deep learning architecture
(Springer Heidelberg, 2022)
Air pollution prediction based on variables in environmental monitoring data gains further importance with increasing concerns about climate change and the sustainability of cities. Modeling of the complex relationships ...
The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things
(Elsevier Ireland Ltd, 2023)
Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, ...
A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain
(Pergamon-Elsevier Science Ltd, 2022)
With the global spread of the COVID-19 epidemic, a reliable method is required for identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits that are both reliable and affordable. Due ...
Regression of Large-Scale Path Loss Parameters Using Deep Neural Networks
(IEEE-Inst Electrical Electronics Engineers Inc, 2022)
Path loss exponent and shadowing factor are among important wireless channel parameters. These parameters can be estimated using field measurements or ray-tracing simulations, which are costly and time-consuming. In this ...
A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder
(Gazi Universitesi, 2023)
Denoising is one of the most important preprocesses in image processing. Noises in images can prevent extracting some important information stored in images. Therefore, before some implementations such as image classification, ...